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This paper presents the results of a study comparing the ability of neural network models and multiple discriminant analysis (MDA) models to predict bond rating changes and to exam if segmentation by investment grade improves classification. Data was collected on more than 900 bonds that had their Standard and Poor's Corporation rating changed during the period 1997 to 2002. This was matched this dataset with corresponding firms which had the same initial bond rating but which did not change. The correspondence was based on the firms being in the same industry, having the same rating at the time of the change (the time frame was one month) and the same approximate asset size (within 20%). This relatively stringent set of criteria reduced the data set to 282 pairs of companies. A neural network model and a multiple discriminant analysis were used to predict both a bond change and the general direction of a movement from a particular bond rating to another bond rating. The predictive variables were financial ratios and rates of change for these ratios. In almost all cases, particularly for the larger sample studies, the neural network models were better predictors than the multiple discriminant models. The paper reviews, in detail, performance of the respective models, strengths and limitations of the models – particularly with respect to underlying assumptions- and future research directions.

This chapter introduces evolutionary neuroscience and its organizational applications, especially its usefulness for motivation analysis in macrolevel disciplines such as strategic management. Macrolevel organizational disciplines have mostly lacked a theory of motivation beyond self-interest assumptions, which fail to explain many important macrolevel organizational phenomena. Evolutionary neuroscience provides an empirically grounded, parsimonious perspective on the human brain and brain evolution which helps clarify the profound complexities of motivation. Evolutionary neuroscience’s theory of the physiological causes of self- and other-interested motivation can support better macrolevel motivation analysis and unify disparate, potentially conflicting motivation theories. Examples are offered of how neuroscience-based motivation theory can support more comprehensive strategic management analysis of competences and competitive advantage.

The global financial crisis (GFC) of 2007–2008 had far-reaching consequences for the global economy, triggering widespread economic turmoil. We use the event-study method to investigate whether investors priced the effect of significant events before the Lehman Brothers' bankruptcy in European and Asia-Pacific banks. Abnormal returns on the event days range from −4.32% to 5.03% in Europe and −5.13% to 6.57% in Asia-Pacific countries. When Lehman Brothers went bankrupt on September 15, 2008, abnormal returns averaged the lowest at −4.32% in Europe and −5.13% in Asia-Pacific countries. The significant abnormal returns show that Lehman Brothers' collapse was a turning point, and investors paid attention to the precrisis events as warning signs of the oncoming crisis.

Microsimulation is a technique that uses the capacity of modern computers to make microunits act and interact in such a way that it is possible to aggregate to the level of interest. A microsimulation model can be seen as a set of rules, which operates on a sample of microunits such as individuals, households, and firms. Each microunit is defined and characterized by a set of properties (variables) and as the model is simulated these properties are updated for each and every microunit. The model might simply be a set of deterministic rules such as the income tax rules of a country operating on a sample of taxpayers, and used to compute the distribution of after-tax income, the aggregate income tax revenue, or other fiscal entities of interest. But the model could also include behavioral assumptions usually formulated as stochastic models. Examples are fertility models, models for household formation and dissolution, labor supply, and mobility.

While recent research has referred to a cognitive view on “business modelling,” it remains unclear in specifying the cognitive foundations of how such modelling happens. This paper proposes building on heuristics as models of individual cognition, which have proved effective foundations of adaptive individual and managerial behaviors. By also drawing on gestalt theory to specify principles of modelling as rule-based form giving, we propose business modelling as a managerial cognitive process of configuring heuristics. The paper makes three contributions. First, we introduce heuristics to the business modelling literature and so provide an established theory of adaptive individual behavior that strengthens the cognitive foundations of business modelling. Second, we conceptualize and theorize on the cognitive activity of business modelling as an iterative process of configuring heuristics by applying gestalt principles. Although the literature on business models has referred to the theories of configurations and gestalt, it has been left to this work to make the theoretical linkages between heuristics, gestalt theory and business modelling explicit. Third, our work contributes to the micro-foundations of the cognitive processes underlying business modelling and thus to broader accounts of adaptive managerial behaviors.